Large language models (LLMs) have demonstrated tremendous capabilities in solving
complex tasks, from quantitative reasoning to understanding natural language. However …

There has been recent interest in using machine-learned predictions to improve the worst-
case guarantees of online algorithms. In this paper we continue this line of work by studying …

In the single-machine non-clairvoyant scheduling problem, the goal is to minimize the total
completion time of jobs whose processing times are unknown a priori. We revisit this well …

The advice model of online computation captures the setting in which the online algorithm is
given some information concerning the request sequence. This paradigm allows to establish …

We consider a natural semi-online model for weighted paging, where at any time the
algorithm is given predictions, possibly with errors, about the next arrival of each page. The …

The online bin packing problem is a well-known optimization challenge that finds application
in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called …

For many application domains, the integration of machine learning (ML) models into
decision making is hindered by the poor explainability and theoretical guarantees of black …

The MinUsageTime Dynamic Bin Packing (DBP) problem aims to minimize the accumulated
bin usage time for packing a sequence of items into bins. It is often used to model job …

In online interval scheduling, the input is an online sequence of intervals, and the goal is to
accept a maximum number of non-overlapping intervals. In the more general disjoint path …

A variant of the online knapsack problem is considered in the settings of trusted and
untrusted predictions. In Unit Profit Knapsack, the items have unit profit, and it is easy to find …